Opinion analysis for business intelligence applications

ABSTRACT

More than ever before, business analysts have access to public forums in which opinions and sentiments about companies, products, and policies are expressed in unstructured form. Mining information from public sources is of great importance to many business intelligence applications such as credit rating or company reputation.

We have implemented a supervised machine-learning system which uses linguistic information to classify text by rating (good or bad, for example, or 1 to 5 stars). In an evaluation we have obtained good results in comparison with the state-of-the-art in opinion mining.

We are further developing the system to classify each text according to a "qualitative variable" category from an ontology specially developed for Business Intelligence (BI). This work will allow us to generate RDF statements to populate a knowledge base for BI.

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INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.1 ADMINISTRATIVE DATA PROCESSING
      Subjects: Business

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition
      I.2.7 Natural Language Processing
          Subjects: Language parsing and understanding

General Terms:
Design

Keywords:
business intelligence, opinion analysis, sentiment extraction